<?xml version="1.0" encoding="utf-8" ?>
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN"
        "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
<head>
  <title>Classifier results - Classifier tests</title>
  <meta http-equiv="Content-Type" content="text/html;charset=utf-8" />
  <meta name="description" content="TestNG unit test results." />
  <link href="reportng.css" rel="stylesheet" type="text/css" />
    <script type="text/javascript" src="reportng.js"></script>
</head>
<body>
<h1>Classifier tests</h1>
<p>
  Test Duration: 378.812s
</p>





  <table class="resultsTable" width="100%">
    <tr><th colspan="3" class="header passed">Passed Tests</th></tr>
              <tr>
        <td colspan="3" class="group">com.meum.classifier.test.FitnessModifierTests</td>
      </tr>
      
<tr>
  <td class="method">
          testEnsembleModifier
      </td>
  <td class="duration">
    18.984s
  </td>
  <td class="result">
        
          <i>Method Arguments: </i><span class="arguments">"gbpeur", EnsembleModifier {negativeContribution=2.0, noChange=1.0, positiveContribution=0.5}, 0.6, 1000, 2, 100, 20</span><br />
    
            <div class="testOutput">
                        TestConfig { <br/>
 gbpeur EnsembleModifier9<br/>
 sub tree max depth: 2<br/>
 population size: 100<br/>
 elite count: 20<br/>
 fitness: The number of incorrectly classified instances. [EnsembleModifier {negativeContribution=2.0, noChange=1.0, positiveContribution=0.5}]<br/>
 operators: [Mutation(probability: 0.6 bias modifier: ConstModifier}, Crossover(sub tree switching), Simplification(replaces decision nodes where both children targets are the same with a single leaf)]<br/>
 observers: [EnsembleModifier {negativeContribution=2.0, noChange=1.0, positiveContribution=0.5}, ConstModifier]<br/>
 random number generator: MersenneTwisterRNG<br/>
 selection strategy: Roulette Wheel Selection<br/>
};<br />
                                (arg14&gt;1.11) ? (SELL) : (BUY)<br />
                                Base fitness: 0.05555555555555555<br />
                                Test set base fitness: 0.6666666666666666<br />
                  </div>
    

      </td>
</tr>
<tr>
  <td class="method">
          testEnsembleModifier
      </td>
  <td class="duration">
    22.047s
  </td>
  <td class="result">
        
          <i>Method Arguments: </i><span class="arguments">"gbpeur", EnsembleModifier {negativeContribution=1.1, noChange=1.0, positiveContribution=0.9}, 0.6, 1000, 2, 100, 20</span><br />
    
            <div class="testOutput">
                        TestConfig { <br/>
 gbpeur EnsembleModifier5<br/>
 sub tree max depth: 2<br/>
 population size: 100<br/>
 elite count: 20<br/>
 fitness: The number of incorrectly classified instances. [EnsembleModifier {negativeContribution=1.1, noChange=1.0, positiveContribution=0.9}]<br/>
 operators: [Mutation(probability: 0.6 bias modifier: ConstModifier}, Crossover(sub tree switching), Simplification(replaces decision nodes where both children targets are the same with a single leaf)]<br/>
 observers: [EnsembleModifier {negativeContribution=1.1, noChange=1.0, positiveContribution=0.9}, ConstModifier]<br/>
 random number generator: MersenneTwisterRNG<br/>
 selection strategy: Roulette Wheel Selection<br/>
};<br />
                                (arg14&gt;1.114) ? ((arg2&gt;1.106) ? ((arg0&gt;1.103) ? (SELL) : ((arg4&gt;1.107) ? (BUY) : (SELL))) : (SELL)) : ((arg0&gt;1.116) ? ((arg7&gt;1.102) ? (SELL) : (BUY)) : ((arg1&gt;1.115) ? ((arg10&gt;1.114) ? (SELL) : (BUY)) : (BUY)))<br />
                                Base fitness: 0.05555555555555555<br />
                                Test set base fitness: 0.6666666666666666<br />
                  </div>
    

      </td>
</tr>
<tr>
  <td class="method">
          testEnsembleModifier
      </td>
  <td class="duration">
    21.907s
  </td>
  <td class="result">
        
          <i>Method Arguments: </i><span class="arguments">"cadeur", EnsembleModifier {negativeContribution=1.1, noChange=0.9, positiveContribution=0.8}, 0.6, 1000, 2, 100, 20</span><br />
    
            <div class="testOutput">
                        TestConfig { <br/>
 cadeur EnsembleModifier12<br/>
 sub tree max depth: 2<br/>
 population size: 100<br/>
 elite count: 20<br/>
 fitness: The number of incorrectly classified instances. [EnsembleModifier {negativeContribution=1.1, noChange=0.9, positiveContribution=0.8}]<br/>
 operators: [Mutation(probability: 0.6 bias modifier: ConstModifier}, Crossover(sub tree switching), Simplification(replaces decision nodes where both children targets are the same with a single leaf)]<br/>
 observers: [EnsembleModifier {negativeContribution=1.1, noChange=0.9, positiveContribution=0.8}, ConstModifier]<br/>
 random number generator: MersenneTwisterRNG<br/>
 selection strategy: Roulette Wheel Selection<br/>
};<br />
                                (arg12&gt;0.632) ? ((arg0&gt;0.628) ? ((arg2&gt;0.641) ? ((arg1&gt;0.633) ? ((arg13&gt;0.653) ? ((arg7&gt;0.648) ? ((arg9&gt;0.661) ? (SELL) : ((arg5&gt;0.624) ? (BUY) : (SELL))) : (SELL)) : (SELL)) : (SELL)) : ((arg4&gt;0.638) ? ((arg1&gt;0.633) ? ((arg13&gt;0.653) ? (BUY) : (SELL)) : ((arg11&gt;0.647) ? (SELL) : (BUY))) : ((arg14&gt;0.653) ? (SELL) : (BUY)))) : ((arg14&gt;0.655) ? (BUY) : ((arg7&gt;0.637) ? (BUY) : (SELL)))) : (BUY)<br />
                                Base fitness: 0.018518518518518517<br />
                                Test set base fitness: 0.2222222222222222<br />
                  </div>
    

      </td>
</tr>
<tr>
  <td class="method">
          testEnsembleModifier
      </td>
  <td class="duration">
    21.407s
  </td>
  <td class="result">
        
          <i>Method Arguments: </i><span class="arguments">"chfeur", EnsembleModifier {negativeContribution=1.1, noChange=0.9, positiveContribution=0.8}, 0.6, 1000, 2, 100, 20</span><br />
    
            <div class="testOutput">
                        TestConfig { <br/>
 chfeur EnsembleModifier2<br/>
 sub tree max depth: 2<br/>
 population size: 100<br/>
 elite count: 20<br/>
 fitness: The number of incorrectly classified instances. [EnsembleModifier {negativeContribution=1.1, noChange=0.9, positiveContribution=0.8}]<br/>
 operators: [Mutation(probability: 0.6 bias modifier: ConstModifier}, Crossover(sub tree switching), Simplification(replaces decision nodes where both children targets are the same with a single leaf)]<br/>
 observers: [EnsembleModifier {negativeContribution=1.1, noChange=0.9, positiveContribution=0.8}, ConstModifier]<br/>
 random number generator: MersenneTwisterRNG<br/>
 selection strategy: Roulette Wheel Selection<br/>
};<br />
                                (arg9&gt;0.669) ? (EVIL) : ((arg10&gt;0.663) ? (BUY) : ((arg13&gt;0.662) ? (EVIL) : ((arg14&gt;0.66) ? ((arg3&gt;0.659) ? ((arg0&gt;0.66) ? ((arg12&gt;0.661) ? (BUY) : (SELL)) : ((arg2&gt;0.66) ? (SELL) : ((arg4&gt;0.66) ? (EVIL) : (BUY)))) : (EVIL)) : ((arg4&gt;0.659) ? ((arg12&gt;0.661) ? (SELL) : (BUY)) : ((arg0&gt;0.659) ? (EVIL) : (BUY))))))<br />
                                Base fitness: 0.037037037037037035<br />
                                Test set base fitness: 0.4444444444444444<br />
                  </div>
    

      </td>
</tr>
<tr>
  <td class="method">
          testEnsembleModifier
      </td>
  <td class="duration">
    19.453s
  </td>
  <td class="result">
        
          <i>Method Arguments: </i><span class="arguments">"chfeur", EnsembleModifier {negativeContribution=2.0, noChange=1.0, positiveContribution=0.5}, 0.6, 1000, 2, 100, 20</span><br />
    
            <div class="testOutput">
                        TestConfig { <br/>
 chfeur EnsembleModifier4<br/>
 sub tree max depth: 2<br/>
 population size: 100<br/>
 elite count: 20<br/>
 fitness: The number of incorrectly classified instances. [EnsembleModifier {negativeContribution=2.0, noChange=1.0, positiveContribution=0.5}]<br/>
 operators: [Mutation(probability: 0.6 bias modifier: ConstModifier}, Crossover(sub tree switching), Simplification(replaces decision nodes where both children targets are the same with a single leaf)]<br/>
 observers: [EnsembleModifier {negativeContribution=2.0, noChange=1.0, positiveContribution=0.5}, ConstModifier]<br/>
 random number generator: MersenneTwisterRNG<br/>
 selection strategy: Roulette Wheel Selection<br/>
};<br />
                                (arg13&gt;0.661) ? (EVIL) : (BUY)<br />
                                Base fitness: 0.037037037037037035<br />
                                Test set base fitness: 0.4444444444444444<br />
                  </div>
    

      </td>
</tr>
<tr>
  <td class="method">
          testEnsembleModifier
      </td>
  <td class="duration">
    21.906s
  </td>
  <td class="result">
        
          <i>Method Arguments: </i><span class="arguments">"gbpeur", EnsembleModifier {negativeContribution=1.2, noChange=1.0, positiveContribution=0.8}, 0.6, 1000, 2, 100, 20</span><br />
    
            <div class="testOutput">
                        TestConfig { <br/>
 gbpeur EnsembleModifier6<br/>
 sub tree max depth: 2<br/>
 population size: 100<br/>
 elite count: 20<br/>
 fitness: The number of incorrectly classified instances. [EnsembleModifier {negativeContribution=1.2, noChange=1.0, positiveContribution=0.8}]<br/>
 operators: [Mutation(probability: 0.6 bias modifier: ConstModifier}, Crossover(sub tree switching), Simplification(replaces decision nodes where both children targets are the same with a single leaf)]<br/>
 observers: [EnsembleModifier {negativeContribution=1.2, noChange=1.0, positiveContribution=0.8}, ConstModifier]<br/>
 random number generator: MersenneTwisterRNG<br/>
 selection strategy: Roulette Wheel Selection<br/>
};<br />
                                (arg6&gt;1.102) ? ((arg13&gt;1.115) ? ((arg14&gt;1.118) ? ((arg9&gt;1.068) ? (SELL) : (BUY)) : ((arg8&gt;1.138) ? (SELL) : ((arg0&gt;1.107) ? ((arg5&gt;1.132) ? (BUY) : (SELL)) : (BUY)))) : ((arg0&gt;1.12) ? (SELL) : ((arg9&gt;1.116) ? ((arg5&gt;1.123) ? (BUY) : (SELL)) : ((arg14&gt;1.114) ? (SELL) : (BUY))))) : (BUY)<br />
                                Base fitness: 0.05555555555555555<br />
                                Test set base fitness: 0.6666666666666666<br />
                  </div>
    

      </td>
</tr>
<tr>
  <td class="method">
          testEnsembleModifier
      </td>
  <td class="duration">
    20.344s
  </td>
  <td class="result">
        
          <i>Method Arguments: </i><span class="arguments">"cadeur", EnsembleModifier {negativeContribution=1.1, noChange=1.0, positiveContribution=0.9}, 0.6, 1000, 2, 100, 20</span><br />
    
            <div class="testOutput">
                        TestConfig { <br/>
 cadeur EnsembleModifier10<br/>
 sub tree max depth: 2<br/>
 population size: 100<br/>
 elite count: 20<br/>
 fitness: The number of incorrectly classified instances. [EnsembleModifier {negativeContribution=1.1, noChange=1.0, positiveContribution=0.9}]<br/>
 operators: [Mutation(probability: 0.6 bias modifier: ConstModifier}, Crossover(sub tree switching), Simplification(replaces decision nodes where both children targets are the same with a single leaf)]<br/>
 observers: [EnsembleModifier {negativeContribution=1.1, noChange=1.0, positiveContribution=0.9}, ConstModifier]<br/>
 random number generator: MersenneTwisterRNG<br/>
 selection strategy: Roulette Wheel Selection<br/>
};<br />
                                (arg12&gt;0.638) ? ((arg14&gt;0.654) ? ((arg1&gt;0.648) ? (SELL) : (BUY)) : ((arg8&gt;0.632) ? (SELL) : (BUY))) : ((arg11&gt;0.634) ? ((arg2&gt;0.653) ? (SELL) : ((arg3&gt;0.638) ? ((arg10&gt;0.625) ? (SELL) : (EVIL)) : (BUY))) : (BUY))<br />
                                Base fitness: 0.018518518518518517<br />
                                Test set base fitness: 0.2222222222222222<br />
                  </div>
    

      </td>
</tr>
<tr>
  <td class="method">
          testEnsembleModifier
      </td>
  <td class="duration">
    21.640s
  </td>
  <td class="result">
        
          <i>Method Arguments: </i><span class="arguments">"chfeur", EnsembleModifier {negativeContribution=0.9, noChange=1.0, positiveContribution=1.1}, 0.6, 1000, 2, 100, 20</span><br />
    
            <div class="testOutput">
                        TestConfig { <br/>
 chfeur EnsembleModifier3<br/>
 sub tree max depth: 2<br/>
 population size: 100<br/>
 elite count: 20<br/>
 fitness: The number of incorrectly classified instances. [EnsembleModifier {negativeContribution=0.9, noChange=1.0, positiveContribution=1.1}]<br/>
 operators: [Mutation(probability: 0.6 bias modifier: ConstModifier}, Crossover(sub tree switching), Simplification(replaces decision nodes where both children targets are the same with a single leaf)]<br/>
 observers: [EnsembleModifier {negativeContribution=0.9, noChange=1.0, positiveContribution=1.1}, ConstModifier]<br/>
 random number generator: MersenneTwisterRNG<br/>
 selection strategy: Roulette Wheel Selection<br/>
};<br />
                                (arg10&gt;0.668) ? (EVIL) : ((arg2&gt;0.662) ? ((arg13&gt;0.663) ? ((arg12&gt;0.662) ? ((arg6&gt;0.66) ? (SELL) : (EVIL)) : ((arg0&gt;0.662) ? ((arg5&gt;0.662) ? (BUY) : (EVIL)) : (SELL))) : (BUY)) : ((arg14&gt;0.661) ? ((arg11&gt;0.663) ? (BUY) : (EVIL)) : ((arg13&gt;0.663) ? (SELL) : (BUY))))<br />
                                Base fitness: 0.037037037037037035<br />
                                Test set base fitness: 0.4444444444444444<br />
                  </div>
    

      </td>
</tr>
<tr>
  <td class="method">
          testEnsembleModifier
      </td>
  <td class="duration">
    21.078s
  </td>
  <td class="result">
        
          <i>Method Arguments: </i><span class="arguments">"cadeur", EnsembleModifier {negativeContribution=1.2, noChange=1.0, positiveContribution=0.8}, 0.6, 1000, 2, 100, 20</span><br />
    
            <div class="testOutput">
                        TestConfig { <br/>
 cadeur EnsembleModifier11<br/>
 sub tree max depth: 2<br/>
 population size: 100<br/>
 elite count: 20<br/>
 fitness: The number of incorrectly classified instances. [EnsembleModifier {negativeContribution=1.2, noChange=1.0, positiveContribution=0.8}]<br/>
 operators: [Mutation(probability: 0.6 bias modifier: ConstModifier}, Crossover(sub tree switching), Simplification(replaces decision nodes where both children targets are the same with a single leaf)]<br/>
 observers: [EnsembleModifier {negativeContribution=1.2, noChange=1.0, positiveContribution=0.8}, ConstModifier]<br/>
 random number generator: MersenneTwisterRNG<br/>
 selection strategy: Roulette Wheel Selection<br/>
};<br />
                                (arg2&gt;0.653) ? (SELL) : ((arg13&gt;0.642) ? ((arg0&gt;0.628) ? ((arg8&gt;0.646) ? (SELL) : ((arg7&gt;0.636) ? ((arg10&gt;0.636) ? (BUY) : ((arg5&gt;0.653) ? (BUY) : (SELL))) : (SELL))) : ((arg6&gt;0.632) ? (SELL) : ((arg14&gt;0.638) ? (BUY) : (SELL)))) : ((arg12&gt;0.637) ? (SELL) : (BUY)))<br />
                                Base fitness: 0.018518518518518517<br />
                                Test set base fitness: 0.2222222222222222<br />
                  </div>
    

      </td>
</tr>
<tr>
  <td class="method">
          testEnsembleModifier
      </td>
  <td class="duration">
    20.281s
  </td>
  <td class="result">
        
          <i>Method Arguments: </i><span class="arguments">"gbpeur", EnsembleModifier {negativeContribution=0.9, noChange=1.0, positiveContribution=1.1}, 0.6, 1000, 2, 100, 20</span><br />
    
            <div class="testOutput">
                        TestConfig { <br/>
 gbpeur EnsembleModifier8<br/>
 sub tree max depth: 2<br/>
 population size: 100<br/>
 elite count: 20<br/>
 fitness: The number of incorrectly classified instances. [EnsembleModifier {negativeContribution=0.9, noChange=1.0, positiveContribution=1.1}]<br/>
 operators: [Mutation(probability: 0.6 bias modifier: ConstModifier}, Crossover(sub tree switching), Simplification(replaces decision nodes where both children targets are the same with a single leaf)]<br/>
 observers: [EnsembleModifier {negativeContribution=0.9, noChange=1.0, positiveContribution=1.1}, ConstModifier]<br/>
 random number generator: MersenneTwisterRNG<br/>
 selection strategy: Roulette Wheel Selection<br/>
};<br />
                                (arg0&gt;1.114) ? (SELL) : ((arg1&gt;1.111) ? ((arg13&gt;1.117) ? ((arg11&gt;1.118) ? (SELL) : (EVIL)) : ((arg3&gt;1.095) ? ((arg7&gt;1.109) ? (BUY) : (SELL)) : ((arg10&gt;1.102) ? (EVIL) : (BUY)))) : ((arg14&gt;1.118) ? ((arg4&gt;1.116) ? (BUY) : (SELL)) : (BUY)))<br />
                                Base fitness: 0.05555555555555555<br />
                                Test set base fitness: 0.6666666666666666<br />
                  </div>
    

      </td>
</tr>
<tr>
  <td class="method">
          testEnsembleModifier
      </td>
  <td class="duration">
    19.203s
  </td>
  <td class="result">
        
          <i>Method Arguments: </i><span class="arguments">"cadeur", EnsembleModifier {negativeContribution=2.0, noChange=1.0, positiveContribution=0.5}, 0.6, 1000, 2, 100, 20</span><br />
    
            <div class="testOutput">
                        TestConfig { <br/>
 cadeur EnsembleModifier14<br/>
 sub tree max depth: 2<br/>
 population size: 100<br/>
 elite count: 20<br/>
 fitness: The number of incorrectly classified instances. [EnsembleModifier {negativeContribution=2.0, noChange=1.0, positiveContribution=0.5}]<br/>
 operators: [Mutation(probability: 0.6 bias modifier: ConstModifier}, Crossover(sub tree switching), Simplification(replaces decision nodes where both children targets are the same with a single leaf)]<br/>
 observers: [EnsembleModifier {negativeContribution=2.0, noChange=1.0, positiveContribution=0.5}, ConstModifier]<br/>
 random number generator: MersenneTwisterRNG<br/>
 selection strategy: Roulette Wheel Selection<br/>
};<br />
                                (arg8&gt;0.645) ? (SELL) : ((arg3&gt;0.648) ? (SELL) : (BUY))<br />
                                Base fitness: 0.018518518518518517<br />
                                Test set base fitness: 0.2222222222222222<br />
                  </div>
    

      </td>
</tr>
<tr>
  <td class="method">
          testEnsembleModifier
      </td>
  <td class="duration">
    24.000s
  </td>
  <td class="result">
        
          <i>Method Arguments: </i><span class="arguments">"chfeur", EnsembleModifier {negativeContribution=1.1, noChange=1.0, positiveContribution=0.9}, 0.6, 1000, 2, 100, 20</span><br />
    
            <div class="testOutput">
                        TestConfig { <br/>
 chfeur EnsembleModifier0<br/>
 sub tree max depth: 2<br/>
 population size: 100<br/>
 elite count: 20<br/>
 fitness: The number of incorrectly classified instances. [EnsembleModifier {negativeContribution=1.1, noChange=1.0, positiveContribution=0.9}]<br/>
 operators: [Mutation(probability: 0.6 bias modifier: ConstModifier}, Crossover(sub tree switching), Simplification(replaces decision nodes where both children targets are the same with a single leaf)]<br/>
 observers: [EnsembleModifier {negativeContribution=1.1, noChange=1.0, positiveContribution=0.9}, ConstModifier]<br/>
 random number generator: MersenneTwisterRNG<br/>
 selection strategy: Roulette Wheel Selection<br/>
};<br />
                                (arg12&gt;0.669) ? (EVIL) : ((arg4&gt;0.663) ? ((arg1&gt;0.661) ? (BUY) : (EVIL)) : ((arg9&gt;0.663) ? ((arg5&gt;0.661) ? (BUY) : (SELL)) : ((arg14&gt;0.661) ? ((arg0&gt;0.663) ? (BUY) : ((arg8&gt;0.661) ? (EVIL) : ((arg11&gt;0.664) ? (BUY) : (SELL)))) : (BUY))))<br />
                                Base fitness: 0.037037037037037035<br />
                                Test set base fitness: 0.4444444444444444<br />
                  </div>
    

      </td>
</tr>
<tr>
  <td class="method">
          testEnsembleModifier
      </td>
  <td class="duration">
    19.843s
  </td>
  <td class="result">
        
          <i>Method Arguments: </i><span class="arguments">"chfeur", EnsembleModifier {negativeContribution=1.2, noChange=1.0, positiveContribution=0.8}, 0.6, 1000, 2, 100, 20</span><br />
    
            <div class="testOutput">
                        TestConfig { <br/>
 chfeur EnsembleModifier1<br/>
 sub tree max depth: 2<br/>
 population size: 100<br/>
 elite count: 20<br/>
 fitness: The number of incorrectly classified instances. [EnsembleModifier {negativeContribution=1.2, noChange=1.0, positiveContribution=0.8}]<br/>
 operators: [Mutation(probability: 0.6 bias modifier: ConstModifier}, Crossover(sub tree switching), Simplification(replaces decision nodes where both children targets are the same with a single leaf)]<br/>
 observers: [EnsembleModifier {negativeContribution=1.2, noChange=1.0, positiveContribution=0.8}, ConstModifier]<br/>
 random number generator: MersenneTwisterRNG<br/>
 selection strategy: Roulette Wheel Selection<br/>
};<br />
                                (arg5&gt;0.663) ? (BUY) : ((arg7&gt;0.659) ? ((arg14&gt;0.661) ? (EVIL) : ((arg9&gt;0.669) ? ((arg8&gt;0.66) ? (SELL) : (BUY)) : ((arg8&gt;0.671) ? (EVIL) : (BUY)))) : (BUY))<br />
                                Base fitness: 0.046296296296296294<br />
                                Test set base fitness: 0.5555555555555556<br />
                  </div>
    

      </td>
</tr>
<tr>
  <td class="method">
          testEnsembleModifier
      </td>
  <td class="duration">
    21.860s
  </td>
  <td class="result">
        
          <i>Method Arguments: </i><span class="arguments">"gbpeur", EnsembleModifier {negativeContribution=1.1, noChange=0.9, positiveContribution=0.8}, 0.6, 1000, 2, 100, 20</span><br />
    
            <div class="testOutput">
                        TestConfig { <br/>
 gbpeur EnsembleModifier7<br/>
 sub tree max depth: 2<br/>
 population size: 100<br/>
 elite count: 20<br/>
 fitness: The number of incorrectly classified instances. [EnsembleModifier {negativeContribution=1.1, noChange=0.9, positiveContribution=0.8}]<br/>
 operators: [Mutation(probability: 0.6 bias modifier: ConstModifier}, Crossover(sub tree switching), Simplification(replaces decision nodes where both children targets are the same with a single leaf)]<br/>
 observers: [EnsembleModifier {negativeContribution=1.1, noChange=0.9, positiveContribution=0.8}, ConstModifier]<br/>
 random number generator: MersenneTwisterRNG<br/>
 selection strategy: Roulette Wheel Selection<br/>
};<br />
                                (arg3&gt;1.088) ? ((arg0&gt;1.114) ? ((arg10&gt;1.102) ? (SELL) : ((arg12&gt;1.095) ? ((arg14&gt;1.115) ? (EVIL) : ((arg13&gt;1.105) ? (SELL) : (BUY))) : (SELL))) : ((arg14&gt;1.115) ? ((arg6&gt;1.095) ? ((arg4&gt;1.072) ? ((arg7&gt;1.12) ? (BUY) : (SELL)) : (SELL)) : (BUY)) : (BUY))) : (BUY)<br />
                                Base fitness: 0.06481481481481481<br />
                                Test set base fitness: 0.7777777777777778<br />
                  </div>
    

      </td>
</tr>
<tr>
  <td class="method">
          testEnsembleModifier
      </td>
  <td class="duration">
    21.031s
  </td>
  <td class="result">
        
          <i>Method Arguments: </i><span class="arguments">"cadeur", EnsembleModifier {negativeContribution=0.9, noChange=1.0, positiveContribution=1.1}, 0.6, 1000, 2, 100, 20</span><br />
    
            <div class="testOutput">
                        TestConfig { <br/>
 cadeur EnsembleModifier13<br/>
 sub tree max depth: 2<br/>
 population size: 100<br/>
 elite count: 20<br/>
 fitness: The number of incorrectly classified instances. [EnsembleModifier {negativeContribution=0.9, noChange=1.0, positiveContribution=1.1}]<br/>
 operators: [Mutation(probability: 0.6 bias modifier: ConstModifier}, Crossover(sub tree switching), Simplification(replaces decision nodes where both children targets are the same with a single leaf)]<br/>
 observers: [EnsembleModifier {negativeContribution=0.9, noChange=1.0, positiveContribution=1.1}, ConstModifier]<br/>
 random number generator: MersenneTwisterRNG<br/>
 selection strategy: Roulette Wheel Selection<br/>
};<br />
                                (arg14&gt;0.633) ? ((arg5&gt;0.635) ? ((arg8&gt;0.654) ? ((arg3&gt;0.647) ? ((arg4&gt;0.638) ? (SELL) : (BUY)) : ((arg9&gt;0.648) ? (BUY) : (SELL))) : (SELL)) : ((arg0&gt;0.628) ? ((arg7&gt;0.639) ? (SELL) : (BUY)) : (SELL))) : ((arg13&gt;0.638) ? (SELL) : (BUY))<br />
                                Base fitness: 0.018518518518518517<br />
                                Test set base fitness: 0.2222222222222222<br />
                  </div>
    

      </td>
</tr>
<tr>
  <td class="method">
          testFunctionModifier
      </td>
  <td class="duration">
    6.969s
  </td>
  <td class="result">
        
          <i>Method Arguments: </i><span class="arguments">"gbpeur", FunctionModifier {function=abs(sin(depth*0.1))}, 0.6, 1000, 2, 1000, 20</span><br />
    
            <div class="testOutput">
                        TestConfig { <br/>
 gbpeur FunctionModifier3<br/>
 sub tree max depth: 2<br/>
 population size: 1000<br/>
 elite count: 20<br/>
 fitness: The number of incorrectly classified instances. [FunctionModifier {function=abs(sin(depth*0.1))}]<br/>
 operators: [Mutation(probability: 0.6 bias modifier: ConstModifier}, Crossover(sub tree switching), Simplification(replaces decision nodes where both children targets are the same with a single leaf)]<br/>
 observers: [FunctionModifier {function=abs(sin(depth*0.1))}, ConstModifier]<br/>
 random number generator: MersenneTwisterRNG<br/>
 selection strategy: Roulette Wheel Selection<br/>
};<br />
                                (arg14&gt;1.114) ? ((arg4&gt;1.095) ? ((arg6&gt;1.095) ? (SELL) : (BUY)) : (BUY)) : ((arg0&gt;1.12) ? (SELL) : ((arg11&gt;1.126) ? (SELL) : ((arg3&gt;1.104) ? ((arg8&gt;1.091) ? (BUY) : (SELL)) : ((arg4&gt;1.103) ? (SELL) : (BUY)))))<br />
                                Base fitness: 0.05555555555555555<br />
                                Test set base fitness: 0.6666666666666666<br />
                  </div>
    

      </td>
</tr>
<tr>
  <td class="method">
          testFunctionModifier
      </td>
  <td class="duration">
    6.875s
  </td>
  <td class="result">
        
          <i>Method Arguments: </i><span class="arguments">"chfeur", FunctionModifier {function=atan(depth*0.1)}, 0.6, 1000, 2, 1000, 20</span><br />
    
            <div class="testOutput">
                        TestConfig { <br/>
 chfeur FunctionModifier2<br/>
 sub tree max depth: 2<br/>
 population size: 1000<br/>
 elite count: 20<br/>
 fitness: The number of incorrectly classified instances. [FunctionModifier {function=atan(depth*0.1)}]<br/>
 operators: [Mutation(probability: 0.6 bias modifier: ConstModifier}, Crossover(sub tree switching), Simplification(replaces decision nodes where both children targets are the same with a single leaf)]<br/>
 observers: [FunctionModifier {function=atan(depth*0.1)}, ConstModifier]<br/>
 random number generator: MersenneTwisterRNG<br/>
 selection strategy: Roulette Wheel Selection<br/>
};<br />
                                (arg3&gt;0.659) ? ((arg6&gt;0.664) ? (EVIL) : ((arg8&gt;0.663) ? ((arg5&gt;0.669) ? (EVIL) : (BUY)) : ((arg11&gt;0.663) ? ((arg9&gt;0.66) ? ((arg5&gt;0.661) ? ((arg0&gt;0.662) ? (EVIL) : (BUY)) : (SELL)) : (SELL)) : ((arg14&gt;0.661) ? ((arg7&gt;0.659) ? ((arg12&gt;0.662) ? (EVIL) : ((arg0&gt;0.662) ? ((arg1&gt;0.662) ? (BUY) : (SELL)) : (SELL))) : (SELL)) : (BUY))))) : (EVIL)<br />
                                Base fitness: 0.037037037037037035<br />
                                Test set base fitness: 0.4444444444444444<br />
                  </div>
    

      </td>
</tr>
<tr>
  <td class="method">
          testFunctionModifier
      </td>
  <td class="duration">
    7.500s
  </td>
  <td class="result">
        
          <i>Method Arguments: </i><span class="arguments">"chfeur", FunctionModifier {function=-depth*0.001}, 0.6, 1000, 2, 1000, 20</span><br />
    
            <div class="testOutput">
                        TestConfig { <br/>
 chfeur FunctionModifier1<br/>
 sub tree max depth: 2<br/>
 population size: 1000<br/>
 elite count: 20<br/>
 fitness: The number of incorrectly classified instances. [FunctionModifier {function=-depth*0.001}]<br/>
 operators: [Mutation(probability: 0.6 bias modifier: ConstModifier}, Crossover(sub tree switching), Simplification(replaces decision nodes where both children targets are the same with a single leaf)]<br/>
 observers: [FunctionModifier {function=-depth*0.001}, ConstModifier]<br/>
 random number generator: MersenneTwisterRNG<br/>
 selection strategy: Roulette Wheel Selection<br/>
};<br />
                                (arg5&gt;0.658) ? ((arg0&gt;0.658) ? ((arg7&gt;0.664) ? (EVIL) : ((arg13&gt;0.662) ? ((arg9&gt;0.662) ? ((arg3&gt;0.662) ? (EVIL) : (BUY)) : (SELL)) : ((arg12&gt;0.669) ? (BUY) : ((arg14&gt;0.661) ? ((arg4&gt;0.662) ? ((arg3&gt;0.662) ? ((arg9&gt;0.662) ? ((arg1&gt;0.661) ? (BUY) : (SELL)) : ((arg10&gt;0.662) ? (EVIL) : ((arg6&gt;0.659) ? ((arg11&gt;0.661) ? (SELL) : ((arg8&gt;0.658) ? ((arg2&gt;0.661) ? ((arg1&gt;0.661) ? (BUY) : (SELL)) : (SELL)) : (SELL))) : (SELL)))) : (EVIL)) : (EVIL)) : (BUY))))) : (EVIL)) : (EVIL)<br />
                                Base fitness: 0.037037037037037035<br />
                                Test set base fitness: 0.4444444444444444<br />
                  </div>
    

      </td>
</tr>
<tr>
  <td class="method">
          testFunctionModifier
      </td>
  <td class="duration">
    7.500s
  </td>
  <td class="result">
        
          <i>Method Arguments: </i><span class="arguments">"cadeur", FunctionModifier {function=depth/generation : generation > 0}, 0.6, 1000, 2, 1000, 20</span><br />
    
            <div class="testOutput">
                        TestConfig { <br/>
 cadeur FunctionModifier5<br/>
 sub tree max depth: 2<br/>
 population size: 1000<br/>
 elite count: 20<br/>
 fitness: The number of incorrectly classified instances. [FunctionModifier {function=depth/generation : generation &gt; 0}]<br/>
 operators: [Mutation(probability: 0.6 bias modifier: ConstModifier}, Crossover(sub tree switching), Simplification(replaces decision nodes where both children targets are the same with a single leaf)]<br/>
 observers: [FunctionModifier {function=depth/generation : generation &gt; 0}, ConstModifier]<br/>
 random number generator: MersenneTwisterRNG<br/>
 selection strategy: Roulette Wheel Selection<br/>
};<br />
                                (arg7&gt;0.661) ? (EVIL) : ((arg4&gt;0.652) ? (SELL) : ((arg8&gt;0.647) ? ((arg13&gt;0.655) ? ((arg1&gt;0.643) ? (BUY) : ((arg2&gt;0.627) ? ((arg10&gt;0.632) ? (SELL) : (BUY)) : (EVIL))) : (SELL)) : ((arg12&gt;0.636) ? ((arg14&gt;0.638) ? ((arg0&gt;0.625) ? ((arg10&gt;0.648) ? ((arg2&gt;0.636) ? (BUY) : (SELL)) : (BUY)) : (SELL)) : ((arg2&gt;0.627) ? ((arg10&gt;0.632) ? (SELL) : ((arg6&gt;0.631) ? (SELL) : (BUY))) : (BUY))) : (BUY))))<br />
                                Base fitness: 0.06481481481481481<br />
                                Test set base fitness: 0.7777777777777778<br />
                  </div>
    

      </td>
</tr>
<tr>
  <td class="method">
          testFunctionModifier
      </td>
  <td class="duration">
    7.078s
  </td>
  <td class="result">
        
          <i>Method Arguments: </i><span class="arguments">"chfeur", FunctionModifier {function=abs(sin(depth*0.1))}, 0.6, 1000, 2, 1000, 20</span><br />
    
            <div class="testOutput">
                        TestConfig { <br/>
 chfeur FunctionModifier0<br/>
 sub tree max depth: 2<br/>
 population size: 1000<br/>
 elite count: 20<br/>
 fitness: The number of incorrectly classified instances. [FunctionModifier {function=abs(sin(depth*0.1))}]<br/>
 operators: [Mutation(probability: 0.6 bias modifier: ConstModifier}, Crossover(sub tree switching), Simplification(replaces decision nodes where both children targets are the same with a single leaf)]<br/>
 observers: [FunctionModifier {function=abs(sin(depth*0.1))}, ConstModifier]<br/>
 random number generator: MersenneTwisterRNG<br/>
 selection strategy: Roulette Wheel Selection<br/>
};<br />
                                (arg3&gt;0.663) ? ((arg6&gt;0.664) ? (EVIL) : (BUY)) : ((arg13&gt;0.663) ? ((arg7&gt;0.664) ? ((arg6&gt;0.664) ? (EVIL) : (BUY)) : ((arg14&gt;0.664) ? (BUY) : (SELL))) : ((arg14&gt;0.661) ? ((arg9&gt;0.661) ? (EVIL) : (SELL)) : (BUY)))<br />
                                Base fitness: 0.037037037037037035<br />
                                Test set base fitness: 0.4444444444444444<br />
                  </div>
    

      </td>
</tr>
<tr>
  <td class="method">
          testFunctionModifier
      </td>
  <td class="duration">
    6.906s
  </td>
  <td class="result">
        
          <i>Method Arguments: </i><span class="arguments">"cadeur", FunctionModifier {function=atan(depth*0.1)}, 0.6, 1000, 2, 1000, 20</span><br />
    
            <div class="testOutput">
                        TestConfig { <br/>
 cadeur FunctionModifier4<br/>
 sub tree max depth: 2<br/>
 population size: 1000<br/>
 elite count: 20<br/>
 fitness: The number of incorrectly classified instances. [FunctionModifier {function=atan(depth*0.1)}]<br/>
 operators: [Mutation(probability: 0.6 bias modifier: ConstModifier}, Crossover(sub tree switching), Simplification(replaces decision nodes where both children targets are the same with a single leaf)]<br/>
 observers: [FunctionModifier {function=atan(depth*0.1)}, ConstModifier]<br/>
 random number generator: MersenneTwisterRNG<br/>
 selection strategy: Roulette Wheel Selection<br/>
};<br />
                                (arg14&gt;0.656) ? ((arg3&gt;0.646) ? ((arg11&gt;0.663) ? ((arg5&gt;0.653) ? ((arg7&gt;0.646) ? (SELL) : ((arg8&gt;0.661) ? (BUY) : (SELL))) : (BUY)) : ((arg5&gt;0.656) ? (BUY) : (SELL))) : (BUY)) : ((arg8&gt;0.647) ? ((arg9&gt;0.633) ? (SELL) : (EVIL)) : ((arg12&gt;0.638) ? ((arg6&gt;0.637) ? ((arg1&gt;0.629) ? ((arg11&gt;0.641) ? (BUY) : (SELL)) : ((arg2&gt;0.631) ? (EVIL) : (SELL))) : (SELL)) : (BUY)))<br />
                                Base fitness: 0.018518518518518517<br />
                                Test set base fitness: 0.2222222222222222<br />
                  </div>
    

      </td>
</tr>
<tr>
  <td class="method">
          testNoOpModifier
      </td>
  <td class="duration">
    7.015s
  </td>
  <td class="result">
        
          <i>Method Arguments: </i><span class="arguments">"gbpeur", NoOpModifier, 0.6, 1000, 2, 1000, 20</span><br />
    
            <div class="testOutput">
                        TestConfig { <br/>
 gbpeur NoOpModifier1<br/>
 sub tree max depth: 2<br/>
 population size: 1000<br/>
 elite count: 20<br/>
 fitness: The number of incorrectly classified instances. [NoOpModifier]<br/>
 operators: [Mutation(probability: 0.6 bias modifier: ConstModifier}, Crossover(sub tree switching), Simplification(replaces decision nodes where both children targets are the same with a single leaf)]<br/>
 observers: [NoOpModifier, ConstModifier]<br/>
 random number generator: MersenneTwisterRNG<br/>
 selection strategy: Roulette Wheel Selection<br/>
};<br />
                                (arg4&gt;1.128) ? (SELL) : ((arg14&gt;1.118) ? ((arg5&gt;1.103) ? (SELL) : ((arg12&gt;1.09) ? ((arg0&gt;1.123) ? (BUY) : ((arg9&gt;1.101) ? (SELL) : (BUY))) : ((arg0&gt;1.123) ? (EVIL) : (SELL)))) : ((arg0&gt;1.123) ? ((arg11&gt;1.11) ? (EVIL) : ((arg13&gt;1.091) ? (BUY) : (SELL))) : (BUY)))<br />
                                Base fitness: 0.05555555555555555<br />
                                Test set base fitness: 0.6666666666666666<br />
                  </div>
    

      </td>
</tr>
<tr>
  <td class="method">
          testNoOpModifier
      </td>
  <td class="duration">
    7.141s
  </td>
  <td class="result">
        
          <i>Method Arguments: </i><span class="arguments">"cadeur", NoOpModifier, 0.6, 1000, 2, 1000, 20</span><br />
    
            <div class="testOutput">
                        TestConfig { <br/>
 cadeur NoOpModifier2<br/>
 sub tree max depth: 2<br/>
 population size: 1000<br/>
 elite count: 20<br/>
 fitness: The number of incorrectly classified instances. [NoOpModifier]<br/>
 operators: [Mutation(probability: 0.6 bias modifier: ConstModifier}, Crossover(sub tree switching), Simplification(replaces decision nodes where both children targets are the same with a single leaf)]<br/>
 observers: [NoOpModifier, ConstModifier]<br/>
 random number generator: MersenneTwisterRNG<br/>
 selection strategy: Roulette Wheel Selection<br/>
};<br />
                                (arg4&gt;0.628) ? ((arg8&gt;0.647) ? ((arg14&gt;0.655) ? ((arg1&gt;0.64) ? ((arg6&gt;0.652) ? ((arg7&gt;0.661) ? (EVIL) : (SELL)) : (BUY)) : (SELL)) : (SELL)) : ((arg3&gt;0.628) ? ((arg12&gt;0.648) ? ((arg1&gt;0.637) ? ((arg9&gt;0.637) ? (BUY) : (SELL)) : (SELL)) : ((arg1&gt;0.628) ? (BUY) : ((arg6&gt;0.634) ? ((arg11&gt;0.64) ? (SELL) : (BUY)) : ((arg0&gt;0.646) ? (BUY) : (SELL))))) : (SELL))) : ((arg11&gt;0.629) ? ((arg1&gt;0.628) ? (BUY) : (SELL)) : (SELL))<br />
                                Base fitness: 0.06481481481481481<br />
                                Test set base fitness: 0.7777777777777778<br />
                  </div>
    

      </td>
</tr>
<tr>
  <td class="method">
          testNoOpModifier
      </td>
  <td class="duration">
    6.813s
  </td>
  <td class="result">
        
          <i>Method Arguments: </i><span class="arguments">"chfeur", NoOpModifier, 0.6, 1000, 2, 1000, 20</span><br />
    
            <div class="testOutput">
                        TestConfig { <br/>
 chfeur NoOpModifier0<br/>
 sub tree max depth: 2<br/>
 population size: 1000<br/>
 elite count: 20<br/>
 fitness: The number of incorrectly classified instances. [NoOpModifier]<br/>
 operators: [Mutation(probability: 0.6 bias modifier: ConstModifier}, Crossover(sub tree switching), Simplification(replaces decision nodes where both children targets are the same with a single leaf)]<br/>
 observers: [NoOpModifier, ConstModifier]<br/>
 random number generator: MersenneTwisterRNG<br/>
 selection strategy: Roulette Wheel Selection<br/>
};<br />
                                (arg12&gt;0.662) ? ((arg6&gt;0.662) ? ((arg9&gt;0.662) ? ((arg1&gt;0.662) ? (EVIL) : ((arg10&gt;0.662) ? (BUY) : (EVIL))) : (SELL)) : ((arg7&gt;0.66) ? ((arg1&gt;0.66) ? ((arg2&gt;0.662) ? ((arg13&gt;0.67) ? (EVIL) : (SELL)) : (BUY)) : (EVIL)) : (SELL))) : ((arg14&gt;0.661) ? (EVIL) : ((arg0&gt;0.658) ? (BUY) : (SELL)))<br />
                                Base fitness: 0.037037037037037035<br />
                                Test set base fitness: 0.4444444444444444<br />
                  </div>
    

      </td>
</tr>
      </table>
  
</body>
</html>
